`sts_zoo.animals`

`LLTFactory`

A local linear trend (LLT) factory.

The generative model for programs created by this factory is

\begin{aligned} l_{t} & = l_{t - 1} + {loc}_{t} + {scale_local}_{t} w_{t}^{l}, l_{0} = ic_local \\ z_{t} & = z_{t - 1} + l_{t} + {scale_level}_{t} w_{t}^{z}, z_{0} = ic_level, \end{aligned}

where $w_{t}^{\cdot} \sim Normal (0, 1)$ , $t_{0} \leq t \leq t_{1}$ .

Args:

loc (Block || torch.tensor || pyro.distributions): location parameter
scale_local (Block || torch.tensor || pyro.distributions): scale parameter for $l_{t}$
scale_level (Block || torch.tensor || pyro.distributions): scale parameter for $z_{t}$
ic_local (torch.tensor || pyro.distributions): initial condition for $l_{t}$
ic_level (torch.tensor || pyro.distributions): initial condition for $z_{t}$

`make`

Creates a new LLT model. The sites in the created model are guaranteed to be unique.

Args:

None

`ModelFactory`

Abstract base class for all model factories. Ensures unique ids for each component of each generated model, and encapsulates model generation through make method.

`make`

Abstract method to be overridden. Calling .make() should create a new stsb3 model with guaranteed unique site names.

`SGTFactory`

A seasonal + global trend (SGT) factory.

The generative model for programs created by this factory is

\begin{aligned} l_{t} & = α_{t} + β_{t} l_{t - 1} + {scale_local}_{t} w_{t} \\ g_{t} & = a + b t \\ s_{t} & = θ_{t \mod s}, s = 1, . . ., num_seasons \\ z_{t} & = s_{t} + g_{t} + l_{t}, \end{aligned}

where $w_{t} \sim Normal (0, 1)$ , $t_{0} \leq t \leq t_{1}$ , and $num_seasons$ is the number of discrete seasons.

Args:

alpha (Block || torch.tensor || pyro.distributions): intercept parameter of $l_{t}$
beta (Block || torch.tensor || pyro.distributions): slope parameter of $l_{t}$
a (Block || torch.tensor || pyro.distributions) intercept parameter of $g_{t}$
b (Block || torch.tensor || pyro.distributions) slope parameter of $g_{t}$
num_seasons (int): $\geq 2$ integer number of seasons, defaults to 2
seasons (torch.tensor || pyro.distributions) season values
scale_local (Block || torch.tensor || pyro.distributions): scale parameter for $l_{t}$
scale_level (Block || torch.tensor || pyro.distributions): scale parameter for $z_{t}$
ic_level (torch.tensor || pyro.distributions): initial condition for $z_{t}$

`make`

Creates a new SGT model. The sites in the created model are guaranteed to be unique.

Args:

None

`SLLTFactory`

A semi-local linear trend (SLLT) factory.

The generative model for programs created by this factory is

\begin{aligned} l_{t} & = α_{t} + β_{t} l_{t - 1} + {scale_local}_{t} w_{t}^{l} \\ z_{t} & = z_{t - 1} + l_{t} + {scale_level}_{t} w_{t}^{z}, z_{0} = ic_level, \end{aligned}

where $w_{t}^{\cdot} \sim Normal (0, 1)$ , $t_{0} \leq t \leq t_{1}$ .

Args:

alpha (Block || torch.tensor || pyro.distributions): intercept parameter of $l_{t}$
beta (Block || torch.tensor || pyro.distributions): slope parameter of $l_{t}$
scale_local (Block || torch.tensor || pyro.distributions): scale parameter for $l_{t}$
scale_level (Block || torch.tensor || pyro.distributions): scale parameter for $z_{t}$
ic_level (torch.tensor || pyro.distributions): initial condition for $z_{t}$

`make`

Creates a new SLLT model. The sites in the created model are guaranteed to be unique.

Args:

None

`StochVolFactory`

A stochastic volatility model factory.

This is a model for the log of an asset price $S_{t}$ . Define $z_{t} = \log S_{t}$ . The generative model for programs created by this factory is

\begin{aligned} isp σ_{t} & = isp σ_{t - 1} + d t_{t} {loc_ispvol}_{t} + \sqrt{d t_{t}} {scale_ispvol}_{t} w_{t}^{isp σ}, l_{0} = ic_logvol \\ z_{t} & = z_{t - 1} + d t_{t} {loc}_{t} + \sqrt{d t_{t}} σ_{t} w_{t}^{z}, z_{0} = ic, \end{aligned}

where $w_{t}^{\cdot} \sim Normal (0, 1)$ , $t_{0} \leq t \leq t_{1}$ , and $isp$ is the inverse softplus function, $isp (y) = \log (- 1 + \exp y)$ .

Args:

dt (Block || torch.tensor || pyro.distributions): time discretization. If a pyro.distributions object, it should be an atomless distribution with non-negative support and will be interpreted as $p (d t) = \prod_{t_{0} \leq t \leq t_{1}} p (d t_{t})$ , not as a single draw. Passing a distributions object can be a useful method for modeling jump-y asset prices (e.g., $p (d t_{t}) = Gamma (d t_{t} | α, β)$ generates the variance gamma process). However, doing this also makes the model non-identifiable in the scale parameter(s).
loc_ispvol (Block || torch.tensor || pyro.distributions): location parameter of the isp volatility process
vol_ispvol (Block || torch.tensor || pyro.distributions): scale/volatility parameter of the isp volatility process
ic_ispvol (torch.tensor || pyro.distributions): initial condition of the isp_volatility process
scale_level (Block || torch.tensor || pyro.distributions): scale parameter for $z_{t}$
loc (Block || torch.tensor || pyro.distributions): location parameter for the log asset price process.
ic (torch.tensor || pyro.distributions): initial condition for the log asset price process

`make`

Creates a new StochVol model. The sites in the created model are guaranteed to be unique.

Args:

None